Use of Natural Language Processing of Patient-Initiated Electronic Health Record Messages to Identify Patients With COVID-19 Infection.

Importance: Natural language processing (NLP) has the potential to enable faster treatment access by reducing clinician response time and improving electronic health record (EHR) efficiency.

Objective: To develop an NLP model that can accurately classify patient-initiated EHR messages and triage COVID-19 cases to reduce clinician response time and improve access to antiviral treatment.

Design, Setting, And Participants: This retrospective cohort study assessed development of a novel NLP framework to classify patient-initiated EHR messages and subsequently evaluate the model's accuracy.

Effect of case and control definitions on genome-wide association study (GWAS) findings.

Genome-wide association studies (GWAS) have significantly advanced our understanding of the genetic underpinnings of diseases, but case and control cohort definitions for a given disease can vary between different published studies. For example, two GWAS for the same disease using the UK Biobank data set might use different data sources (i.e.

Regularization of Deep Neural Networks for EEG Seizure Detection to Mitigate Overfitting.

Seizure detection is a major goal for simplifying the workflow of clinicians working on EEG records. Current algorithms can only detect seizures effectively for patients already presented to the classifier. These algorithms are hard to generalize outside the initial training set without proper regularization and fail to capture seizures from the larger population.

Discovery of Lipidome Alterations Following Traumatic Brain Injury via High-Resolution Metabolomics.

Traumatic brain injury (TBI) can occur across wide segments of the population, presenting in a heterogeneous manner that makes diagnosis inconsistent and management challenging. Biomarkers offer the potential to objectively identify injury status, severity, and phenotype by measuring the relative concentrations of endogenous molecules in readily accessible biofluids. Through a data-driven, discovery approach, novel biomarker candidates for TBI were identified in the serum lipidome of adult male Sprague-Dawley rats in the first week following moderate controlled cortical impact (CCI).

Causes of death in the United States, 1999 to 2014.

Statistical methods have been widely used in studies of public health. Although useful in clinical research and public health policy making, these methods could not find correlation among health conditions automatically, or capture the temporal evolution of causes of death correctly. To cope with two challenges above, we implement an unsupervised machine learning model, termed topic models, to investigate the mortality data of the United States.

Intraoperative Spectroscopy with Ultrahigh Sensitivity for Image-Guided Surgery of Malignant Brain Tumors.

Intraoperative cancer imaging and fluorescence-guided surgery have attracted considerable interest because fluorescence signals can provide real-time guidance to assist a surgeon in differentiating cancerous and normal tissues. Recent advances have led to the clinical use of a natural fluorophore called protoporphyrin IX (PpIX) for image-guided surgical resection of high-grade brain tumors (glioblastomas). However, traditional fluorescence imaging methods have only limited detection sensitivity and identification accuracy and are unable to detect low-grade or diffuse infiltrating gliomas (DIGs).

icuARM-An ICU Clinical Decision Support System Using Association Rule Mining.

The rapid development of biomedical monitoring technologies has enabled modern intensive care units (ICUs) to gather vast amounts of multimodal measurement data about their patients. However, processing large volumes of complex data in real-time has become a big challenge. Together with ICU physicians, we have designed and developed an ICU clinical decision support system icuARM based on associate rule mining (ARM), and a publicly available research database MIMIC-II (Multi-parameter Intelligent Monitoring in Intensive Care II) that contains more than 40,000 ICU records for 30,000+patients.

Multiscale integration of -omic, imaging, and clinical data in biomedical informatics.

This paper reviews challenges and opportunities in multiscale data integration for biomedical informatics. Biomedical data can come from different biological origins, data acquisition technologies, and clinical applications. Integrating such data across multiple scales (e.

In the spotlight: Bioinformatics.

During 2012, next generation sequencing (NGS) has attracted great attention in the biomedical research community, especially for personalized medicine. Also, third generation sequencing has become available. Therefore, state-of-art sequencing technology and analysis are reviewed in this Bioinformatics spotlight on 2012.

Integrating image analysis algorithms in a web interface for the quantification of microtubule dynamics.

We present improvements to a web interface and an integrated computational tracking algorithm for quantitative analysis of microtubule dynamics in live-cell microscopy images. Based on a previously implemented system, more new functionalities have been added to the interface. The system also integrates a computational tracking algorithm to aid the analysis.

Cardiovascular genomics: a biomarker identification pipeline.

Genomic biomarkers are essential for understanding the underlying molecular basis of human diseases such as cardiovascular disease. In this review, we describe a biomarker identification pipeline for cardiovascular disease, which includes 1) high-throughput genomic data acquisition, 2) preprocessing and normalization of data, 3) exploratory analysis, 4) feature selection, 5) classification, and 6) interpretation and validation of candidate biomarkers. We review each step in the pipeline, presenting current and widely used bioinformatics methods.

Combining two-dimensional diffusion-ordered nuclear magnetic resonance spectroscopy, imaging desorption electrospray ionization mass spectrometry, and direct analysis in real-time mass spectrometry for the integral investigation of counterfeit pharmaceuticals.

During the past decade, there has been a marked increase in the number of reported cases involving counterfeit medicines in developing and developed countries. Particularly, artesunate-based antimalarial drugs have been targeted, because of their high demand and cost. Counterfeit antimalarials can cause death and can contribute to the growing problem of drug resistance, particularly in southeast Asia.

Sphingolipidomics: a valuable tool for understanding the roles of sphingolipids in biology and disease.

The sphingolipidome is the portion of the lipidome that encompasses all sphingoid bases and their derivatives. Whereas the most studied sphingoid base is sphingosine [(2S,3R,4E)-2-aminooctadecene-1,3-diol], mammals have dozens of structural variants, and hundreds of additional types have been found in other eukaryotic organisms and some bacteria and viruses. Multiplying these figures by the N-acyl-derivatives ("ceramides") and the more than 500 phospho- and glyco- headgroups places the number of discrete molecular species in the tens of thousands or higher.

(Glyco)sphingolipidology: an amazing challenge and opportunity for systems biology.

Sphingolipids are found in essentially all eukaryotes and in some prokaryotes and viruses, where they influence cell structure, signaling and interactions with the extracellular environment. Because of the combinatorial nature of their biosynthesis, the sphingolipidome comprises untold thousands of species that encompass bioactive backbones and complex phospho- and glycolipids. Mass spectrometry is able to analyze a growing fraction of the sphingolipidome and is beginning to provide information about localization.